Type Members

1.  class LegalBertForSequenceClassification extends MedicalBertForSequenceClassification

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

   Pretrained models can be loaded with pretrained of the companion object:

   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "bert_sequence_classifier_ade", if no name is provided.

   For available pretrained models please see the Models Hub.

   Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base._
   import com.johnsnowlabs.nlp.annotator._
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     sequenceClassifier
   ))
   
   val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.select("label.result").show(false)
   +--------------------+
   |result              |
   +--------------------+
   |[True, False]       |
   +--------------------+

   See also

   MedicalBertForSequenceClassification for sequnece-level classification

   Annotators Main Page for a list of transformer based classifiers

2.  class LegalClassifierDLApproach extends ClassifierDLApproach

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

   Pretrained models can be loaded with pretrained of the companion object:

   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "bert_sequence_classifier_ade", if no name is provided.

   For available pretrained models please see the Models Hub.

   Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base._
   import com.johnsnowlabs.nlp.annotator._
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     sequenceClassifier
   ))
   
   val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.select("label.result").show(false)
   +--------------------+
   |result              |
   +--------------------+
   |[True, False]       |
   +--------------------+

   See also

   MedicalBertForSequenceClassification for sequnece-level classification

   Annotators Main Page for a list of transformer based classifiers

3.  class LegalClassifierDLModel extends ClassifierDLModel

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g.

   MedicalBertForSequenceClassification can load Bert Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

   Pretrained models can be loaded with pretrained of the companion object:

   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")

   The default model is "bert_sequence_classifier_ade", if no name is provided.

   For available pretrained models please see the Models Hub.

   Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

   Example

   import spark.implicits._
   import com.johnsnowlabs.nlp.base._
   import com.johnsnowlabs.nlp.annotator._
   import org.apache.spark.ml.Pipeline
   
   val documentAssembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val sequenceClassifier = MedicalBertForSequenceClassification.pretrained()
     .setInputCols("token", "document")
     .setOutputCol("label")
     .setCaseSensitive(true)
   
   val pipeline = new Pipeline().setStages(Array(
     documentAssembler,
     tokenizer,
     sequenceClassifier
   ))
   
   val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
   val result = pipeline.fit(data).transform(data)
   
   result.select("label.result").show(false)
   +--------------------+
   |result              |
   +--------------------+
   |[True, False]       |
   +--------------------+

   See also

   MedicalBertForSequenceClassification for sequnece-level classification

   Annotators Main Page for a list of transformer based classifiers

4.  class LegalDocumentMLClassifierApproach extends DocumentMLClassifierApproach

   Trains a model to classify documents with a Logarithmic Regression algorithm.

   Trains a model to classify documents with a Logarithmic Regression algorithm. Training data requires columns for text and their label. The result is a trained DocumentLogRegClassifierModel.

   Example

   Define pipeline stages to prepare the data

   val document_assembler = new DocumentAssembler()
     .setInputCol("text")
     .setOutputCol("document")
   
   val tokenizer = new Tokenizer()
     .setInputCols("document")
     .setOutputCol("token")
   
   val normalizer = new Normalizer()
     .setInputCols("token")
     .setOutputCol("normalized")
   
   val stopwords_cleaner = new StopWordsCleaner()
     .setInputCols("normalized")
     .setOutputCol("cleanTokens")
     .setCaseSensitive(false)
   
   val stemmer = new Stemmer()
     .setInputCols("cleanTokens")
     .setOutputCol("stem")

   Define the document classifier and fit training data to it

   val logreg = new DocumentLogRegClassifierApproach()
     .setInputCols("stem")
     .setLabelCol("category")
     .setOutputCol("prediction")
   
   val pipeline = new Pipeline().setStages(Array(
     document_assembler,
     tokenizer,
     normalizer,
     stopwords_cleaner,
     stemmer,
     logreg
   ))
   
   val model = pipeline.fit(trainingData)

   See also

   DocumentLogRegClassifierModel for instantiated models

5.  class LegalDocumentMLClassifierModel extends DocumentMLClassifierModel

   Classifies documents with a Logarithmic Regression algorithm.

   Classifies documents with a Logarithmic Regression algorithm. Currently there are no pretrained models available. Please see DocumentLogRegClassifierApproach to train your own model.

   Please check out the Models Hub for available models in the future.

6.  class LegalFewShotClassifierApproach extends FewShotClassifierApproach
7.  class LegalFewShotClassifierModel extends FewShotClassifierModel
8.  trait ReadLegalBertForSequenceClassificationTensorflowModel extends ReadTensorflowModel
9.  trait ReadablePretrainedLegalBertForSequenceModel extends ParamsAndFeaturesReadable[LegalBertForSequenceClassification] with HasPretrained[LegalBertForSequenceClassification]